Foundations of LLMOps: Deploy, Version, and Monitor LLMs

As Large Language Models (LLMs) reshape the tech landscape, the ability to reliably bring these models into production is becoming a highly sought-after skill. This foundational course guides you through the essential practices of LLMOps. You will transition from understanding basic AI concepts to confidently managing the lifecycle of an LLM application, focusing on deployment, continuous monitoring, and version control. What you will learn: • Understand the core principles of LLMOps and how they differ from traditional software development. • Deploy large language models into production environments using modern infrastructure-as-code basics. • Implement effective monitoring strategies to track model performance, latency, and response quality. • Manage model versioning and prompt variations to ensure consistent and reliable application behavior. • Explore fundamental Retrieval-Augmented Generation (RAG) patterns and how to maintain them in production. • Apply basic security and cost-management practices essential for running LLMs at scale. The course begins by establishing key terminology and foundational concepts of machine learning operations before moving into practical, written workflows on deployment and monitoring. Designed entirely for beginners, this course requires no prior experience in DevOps or machine learning. Start reading today to build your expertise in managing modern AI applications.